Spatial planning potential for reducing natural risks including wildfires is widely recognized. This research is focused on Portugal, a wildfire-prone country in southern Europe, where the competencies for spatial planning lie on four geographical levels: (i) the national and regional levels, with a strategic nature, set the general goals or the agenda of principles for spatial planning and (ii) the inter-municipal and municipal levels use regulative land-use planning instruments. There is a trend to bring together spatial planning and wildfire management policies.

Confirmation of rights and collective trust (interpersonal and institutional) can act as primary factors for facilitating effective forest management and conservation. Collective forests are lands held collectively by either rural or indigenous communities based on a shared history, language, culture, or lineage. It is an institutional arrangement in which communities are involved wholly or partly in decision-making and contribute knowledge and labour to achieve healthy forests and social well-being.

This paper addresses a forest harvesting problem with adjacency constraints, including additional environmental constraints to protect wildlife habitats and minimize infrastructure deployment costs. To this end, we propose an integer programming model to include those considerations during the optimization of the harvest regime of a Mexican forest. The model considered was based on the Unit Restriction Model, a benchmark approach that merges the management units before the optimization process.